Sequencing yeast lines to measure rates of neutral and deleterious mutations

描述(申请人提供)：自发突变是个体之间遗传差异的最终原因，因此是理解进化过程和许多人类疾病的关键。然而，突变的速度和模式很难衡量，因为突变很罕见，而且立即受到自然选择的影响。DNA测序技术的最新进展，加上对适应性成分(如生长速度)的高通量分析的发展，为获得对自发突变频谱的更精确和更全面的看法提供了独特的机会。突变率和模式可以使用突变积累(MA)线直接测量，这些突变积累线是在实验室通过多次重复的种群瓶颈构建的。这些瓶颈使有效种群规模保持在较低水平，从而阻止了自然选择清除有害突变。在这个项目中，将使用149个具有良好遗传特性的酵母物种的二倍体MA系，酿酒酵母。这些品系传代了2100代，因此总共捕获了300,000个细胞分裂(单倍体基因组的600,000个复制)。单倍体已经被用来估计酵母的突变谱，但二倍体有几个关键的优点，包括更好地屏蔽有害突变，避免基因组不稳定性，以及便于下游的遗传分析。在目标1中，将使用下一代(Illumina)技术获得所有149个MA系及其共同祖先系的完整基因组序列。这将产生比之前获得的关于自发突变的几乎两个数量级的直接数据。在目标2中，将进行遗传分析，以确定每个携带高度有害突变的二倍体MA系。对于每个这样的品系，对汇集的单倍体后代进行高覆盖率测序将从分子上识别高度有害的突变。在目标3中，将对每个二倍体MA系的单倍体后代进行高通量生长率分析。增长率分析将提供对自发突变的边际适应效应分布的估计。这将是一个重大进步，因为有害突变的速度通常无法直接测量，但却是进化理论模型中的一个基本参数。中度有害突变将通过对汇集的单倍体后代进行高复盖率测序来从分子上识别。在目标4中，将建立96个交配型a型单倍体品系的集合，每个单倍体品系来自不同的二倍体MA系，作为研究突变对复杂性状影响的社区资源。每一株系的完整基因将通过测序获得。为了最大限度地利用这96个品系，还将制作两套品系：交配型a单倍体和纯合a/a二倍体。该项目将通过加快未来对突变及其表型效应之间联系的研究，对数量遗传学、系统生物学以及进化遗传学和基因组学的研究产生直接和重大的影响。

DESCRIPTION (provided by applicant): Spontaneous mutations are the ultimate cause of genetic differences between individuals, and are therefore key to understanding the evolutionary process and many human diseases. However, the rates and patterns of mutation are difficult to measure because mutations are rare and are immediately subjected to natural selection. Recent advances in DNA sequencing technology, combined with the development of high- throughput assays of components of fitness, such as growth rate, present a unique opportunity to obtain a dramatically more precise and comprehensive view of the spectrum of spontaneous mutations. Mutation rates and patterns can be measured directly using mutation-accumulation (MA) lines, which are constructed in the laboratory by many generations of repeated population bottlenecking. The bottlenecks keep effective population size low and therefore prevent natural selection from purging deleterious mutations. In this project, a collection of 149 diploid MA lines of the genetically well-characterized yeast species, Saccharomyces cerevisiae, will be used. The lines were passaged for 2100 generations and therefore collectively capture over 300,000 cell divisions (600,000 replications of a haploid genome). Haploids have been used previously to estimate mutational spectra in yeast, but diploidy has several critical advantages, including better shielding of deleterious mutations, avoidance of genomic instability, and facilitation of downstream genetic analyses. In Aim 1, the complete genome sequences of all 149 MA lines and their common ancestral line will be obtained using next-generation (Illumina) technology. This will yield almost two orders of magnitude more direct data on spontaneous mutations than previously achieved. In Aim 2, genetic analysis will be performed to identify each diploid MA line that carries a highly deleterious mutation. For each such line, high-coverage sequencing of pooled haploid progeny will identify the highly deleterious mutation molecularly. In Aim 3, high-throughput growth-rate assays will be performed on haploid progeny from each diploid MA line. The growth-rate assays will provide an estimate of the distribution of marginal fitness effects of spontaneous mutations. This will be a major advance because the rate of deleterious mutations is typically inaccessible to direct measurement, yet is a fundamental parameter in theoretical models of evolution. Moderately deleterious mutations will be identified molecularly by high-coverage sequencing of pooled haploid progeny. In Aim 4, a collection of 96 haploid lines of mating-type a, each derived from a different diploid MA line, will be established, as a community resource for studying the effects of mutations on complex traits. The complete genotype of each line will be obtained by sequencing. For maximum utility, two sets of lines derived from these 96 lines will also be made: haploids of mating-type a and homozygous a/a diploids. This project will have an immediate and major impact on research in quantitative genetics, systems biology and evolutionary genetics and genomics, by accelerating future investigations of the links between mutations and their phenotypic effects.